Cybersecurity is a field in which integration of artificial intelligence (AI) represents a significant direction towards protection against cyber threats. This scoping review explores the current impact and future prospects of AI in four key areas of cybersecurity: threat detection, endpoint security, phishing and fraud detection, and network security. The main goal was to answer the research question, ‘Is AI an effective method to enhance current infrastructures’ cybersecurity?’ Method: Through the PRISMA-ScR protocol, 2548 records were identified from the Google Scholar database from January 2020 to April 2025. The following search terms were used to identify available literature: “Artificial Intelligence Cybersecurity”, “Machine Learning Cybersecurity”, “Cybersecurity Innovation AI”, “AI Future Perspective Cybersecurity”, “Machine Learning Innovation Cybersecurity”. The search only included articles in English. No grey literature has been included. Articles with a focus on performance optimization, cost analysis and business models without a focus on privacy and security have been discarded. Results: The impact and performance of AI algorithms have been highlighted through a selection of 20 articles. Both Machine Learning and Neural Network methods have been employed in the literature, with Decision Trees and Random Forest being the most common approaches. Discussion: The main common limitations of the analyzed articles have been discussed, highlighting possible future directions of research to tackle them. Conclusions: Despite the evidenced limitations, AI showed promising results in improving cybersecurity, especially concerning cyber attack detection and classification, with methods able to grant very high accuracy and trustworthiness.
Randieri et al. (2025) studied this question.